Ollama VRAM management stalls RTX 3060 setups

The "one-click" simplicity of local AI wrappers is masking a critical lack of granular resource control for consumer-grade GPUs.

• –Ollama's silent fallback to system RAM when VRAM limits are approached creates a performance cliff that is difficult for non-technical users to diagnose.
• –Background features in Open WebUI, such as title and tag auto-generation, can consume enough VRAM to unexpectedly push model layers onto the CPU.
• –12GB VRAM is technically sufficient for 14B models at 4-bit quantization, but the "hidden" overhead of the inference stack and context window is narrowing the usable budget.
• –Enthusiasts are increasingly reverting to manual Modelfile overrides or lower-level backends like llama.cpp to force hardware utilization.